{"id":"W2560512335","doi":"10.3390/risks4040046","title":"Deflation Risk and Implications for Life Insurers","year":2016,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; HEC Montréal; Society of Actuaries","keywords":"Deflation; Economics; Inflation (cosmology); Life insurance; Interest rate; Econometrics; Investment (military); Variance (accounting); Real interest rate; Monetary economics; Actuarial science; Financial economics; Monetary policy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005856934,0.000231716,0.0004147253,0.001101584,0.000713039,0.002433202,0.000554189,0.001494452,0.00297292],"category_scores_gemma":[0.04062075,0.0001759109,0.0004987992,0.0007269491,0.00229866,0.002798697,0.002000334,0.002356533,0.0001223359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001110802,"about_ca_system_score_gemma":0.0006979303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001613959,"about_ca_topic_score_gemma":0.001181195,"domain_scores_codex":[0.9978027,0.001205056,0.000135484,0.0002253056,0.00044505,0.0001863561],"domain_scores_gemma":[0.9712356,0.01948387,0.006301215,0.0009969276,0.001164584,0.0008176762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003895096,0.0003458354,0.2115755,0.0002253844,0.0002030567,0.001239073,0.00430996,0.03738112,0.001986485,0.6489823,0.003221847,0.09014],"study_design_scores_gemma":[0.00002586264,0.0002895958,0.1200833,0.0002933218,0.00008882254,0.001408298,0.003727106,0.1369994,0.001360553,0.7302548,0.005379669,0.00008921884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9018112,0.004853975,0.04888358,0.02213476,0.0001206969,0.0000368252,0.0001950475,0.00005965428,0.0219043],"genre_scores_gemma":[0.9978107,0.0004550037,0.001137871,0.000143956,0.00006183787,0.00000502383,0.00002108517,0.000004420645,0.0003601729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005856934,"threshold_uncertainty_score":0.03097481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06686069432047792,"score_gpt":0.3674067590846645,"score_spread":0.3005460647641865,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}